Decoupling of economic growth from CO₂ emissions in Yangtze River Economic Belt cities

Cities play significant roles in mitigating global climate change and formulating low carbon roadmaps. As the first regional strategy that prioritizes green development, the Yangtze River Economic Belt (YREB) is an economic circle along the Yangtze River, stringing up 11 provinces and municipalities from west to east of China. The huge regional heterogeneity in terms of economic development, size, and structure in YREB cities need differentiated emission reduction strategies and low-carbon development pathways. This study compiled the CO2 emission inventories of 85 cities in the YREB for the first time and explored the decoupling of economic growth from CO2 emissions at the city level. The results show that CO2 emissions of YREB cities increased at an annual average rate of 5.1% from 2005 to 2017, and 85 YREB cities emitted 44% of national total CO2 emissions and contributed 41% of national GDP in 2017. 61% of cities dominated by high-tech and service industry achieved decoupling between economic development and emissions before 2009 and are moving forward to a stronger decoupling state. 25% of cities achieved decoupling after 2009 and these post-decoupling cities took the heavy industry and light industry as their leading industries. Resource-based cities with slow economic development and high CO2 emissions changed from decoupling to negative decoupling or coupling. The proposed differentiated low-carbon development pathways for YREB cities could provide references for cities at different stages to achieve decoupling of GDP from CO2 emissions and emission reduction goals

Trends, Drivers, and Mitigation of CO₂ Emissions in the Guangdong–Hong Kong–Macao Greater Bay Area

The Guangdong–Hong Kong–Macao Greater Bay Area (GBA) is a national initiative aimed at building a world-class city cluster in China and whose trends, socioeconomic drivers of CO2 emissions, and mitigation pathways are of great significance to the high-quality regional economic development. This study compiled the CO2 emission inventories of the GBA from 2000 to 2019 and explored the key drivers of CO2 emissions using the logarithmic mean Divisia index method. The results showed that CO2 emissions in GBA slowed significantly after 2017 and have already been decoupled from gross domestic product (GDP) growth. Economic growth and energy intensity are the major factors driving and inhibiting the increase in GBA's CO2 emissions, respectively. The energy production and heavy manufacturing sectors have reduced their roles in driving the growth of GBA's CO2 emissions. GBA achieved remarkable results in low-carbon development through industrial restructuring and upgrading. Industrial upgrades in Shenzhen and Hong Kong and technological advances in Shenzhen, Guangzhou, and Foshan have significantly curbed the growth in the GBA's CO2 emissions. The heterogeneity of cities in the GBA greatly increases the complexity of formalizing the allocation of emission reduction tasks and developing a roadmap for regional carbon neutrality. Graded emission reduction strategies and carbon peaking and neutrality policy recommendations for GBA cities are proposed. This study provides a scientific basis for the development of an action program for carbon peaking and neutrality in GBA cities and low-carbon development plans for other cities and regions.</p

ExonImpact: prioritizing pathogenic alternative splicing events

Alternative splicing (AS) is a closely regulated process that allows a single gene to encode multiple protein isoforms, thereby contributing to the diversity of the proteome. Dysregulation of the splicing process has been found to be associated with many inherited diseases. However, among the pathogenic AS events, there are numerous “passenger” events whose inclusion or exclusion does not lead to significant changes with respect to protein function. In this study, we evaluate the secondary and tertiary structural features of proteins associated with disease-causing and neutral AS events, and show that several structural features are strongly associated with the pathological impact of exon inclusion. We further develop a machine-learning-based computational model, ExonImpact, for prioritizing and evaluating the functional consequences of hitherto uncharacterized AS events. We evaluated our model using several strategies including cross-validation, and data from the Gene-Tissue Expression (GTEx) and ClinVar databases. ExonImpact is freely available at http://watson.compbio.iupui.edu/ExonImpact